Radiation therapy is a widely used modality for treating malignancies in e.g. brain, breast, lung,
prostate and the head and neck (H&N) region. The search for the optimal radiation treatment approach
for a specific patient is a complex task, ultimately seeking to maximize the tumour control probability
(TCP) while minimizing the normal tissue complication probabilities (NTCP). Conventionally, the
radiation quality used to achieve this has been photons of various energies. In the modern treatment
era, photon energy spectrums in the megavoltage region delivering 2 Gy per fraction in approximately
20 to 40 daily fractions has been the standard curative treatment regime. However, during the last
decades, the interest in hypofractionated treatments and proton therapy has rapidly increased. This
adds complexity to the plan selection process, as decision criteria are needed to determine which
patients that are eligible for hypofractionated treatment and/or proton therapy. For the latter, the focus
has been on the possibility of lowering the normal tissue toxicity compared with conventional photon
therapy. Given the same TCP for a photon and a proton plan, the plan selection could then be made
purely based on the reduction in NTCP. Since photon therapy is substantially cheaper than proton
therapy and far more treatment units are being available, the proton plan must demonstrate a
substantial NTCP reduction in order to be selected for treatment. Such a plan selection system between
photon and proton plans is clean and elegant, but is not flawless. The nominal plans are typically
optimized on a single CT scan of the patient and do not account for all the uncertainties during
treatment delivery related to patient setup, breathing motion, proton range etc. It also relies on some
modelling of the relative biological effectiveness (RBE) between photons and protons as their energy
deposition characteristics differ. The clinical standard of using a constant proton RBE of 1.1 does not
reflect the complex nature of the RBE, which varies with parameters such as fractionation dose, linear
energy transfer, tissue type and biological endpoint.
These aspects of proton plan evaluation and selection have been investigated in this thesis
through three individual studies, papers I, II and III. Paper I investigates the impact of including
variable RBE models in the plan comparison between proton and photon prostate plans for various
fractionation schedules. It also presents a pragmatic re-optimization method of proton plans, which
accounts for the variability in the RBE. In paper II, a method of incorporating RBE model
uncertainties into the plan robustness evaluation is proposed and subsequently applied on three
treatment sites using two RBE models. Paper III evaluates the impact of variable RBE models and
breathing motion for breast cancer treatments when comparing photon and proton plans.
The results from papers I, II and III indicate that the inclusion of variable RBE models and their
uncertainties into the proton plan evaluation could lead to differences from the nominal plans made
under the assumption of a constant RBE of 1.1 for both target and normal tissue doses. The dose for
high α/β targets (e.g. H&N tumours) was predicted to be slightly lower, whereas the opposite was
predicted for low α/β targets (e.g. breast and prostate) in comparison to the nominal dose. For most
normal tissues, the predicted doses were often substantially higher, resulting in higher NTCP
estimates. By combining uncertainties in patient setup, range and breathing motion with RBE
uncertainties, comprehensive robustness evaluations could be performed and possibly be included in
the plan selection process in the search for the optimal treatment approach.